EXPLAINABLE AI IN RECOMMENDATIONS: ANALYZING THE NEED FOR TRANSPARENT RECOMMENDATION SYSTEMS

Authors

  • Alpamis Kutlimuratov Toshkent Kimyo Xalqaro Universiteti
  • Farrux Zafar o‘g‘li Baxritdinov Toshkent Kimyo Xalqaro Universiteti

Keywords:

Recommendation system, Explainable AI, Black box, Bias, Model interpretability.

Abstract

In an age where artificial intelligence (AI) drives many aspects of our digital experiences, the call for transparency in AI models, particularly in recommendation systems, has become louder. This paper dives deep into the burgeoning field of Explainable AI (XAI) in the context of recommendation systems, emphasizing its importance and potential implementations.

References

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Kutlimuratov, A.; Abdusalomov, A.B.; Oteniyazov, R.; Mirzakhalilov, S.; Whangbo, T.K. Modeling and Applying Implicit Dormant Features for Recommendation via Clustering and Deep Factorization. Sensors 2022, 22, 8224. https://doi.org/10.3390/s22218224.

Alpamis Kutlimuratov, Nozima Atadjanova. (2023). MOVIE RECOMMENDER SYSTEM USING CONVOLUTIONAL NEURAL NETWORKS ALGORITHM. https://doi.org/10.5281/zenodo.7854603

Alpamis Kutlimuratov, Makhliyo Turaeva. (2023). MUSIC RECOMMENDER SYSTEM. https://doi.org/10.5281/zenodo.7854462

Alpamis Kutlimuratov, Jamshid Khamzaev, Dilnoza Gaybnazarova. (2023). THE PROCESS OF DEVELOPING PERSONALIZED TRAVEL RECOMMENDATIONS. https://doi.org/10.5281/zenodo.7858377

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Published

2023-12-30

How to Cite

Kutlimuratov, A., & Baxritdinov , F. Z. o‘g‘li. (2023). EXPLAINABLE AI IN RECOMMENDATIONS: ANALYZING THE NEED FOR TRANSPARENT RECOMMENDATION SYSTEMS. Innovative Development in Educational Activities, 2(24), 68–74. Retrieved from https://openidea.uz/index.php/idea/article/view/1943